Real-time Disruption Research Articles

Disruption detection for a cognitive digital supply chain twin using hybrid deep learning

Recent disruptive events, such as COVID-19 and Russia–Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue.

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals.

RETRACTED: Data-driven model predictive control for real-time planned lead time optimization in a reconfigurable flow line

Mass customization has enabled smart manufacturing systems to develop reconfigurable machines and fixtures to fulfill customized products with high process variety. Coupled with these reconfigurable components, diversified design and operational problems should be dealt with such that the efficiency of reconfigurations can be maximized. This paper investigates a planned lead time (PLT) optimization problem after addressing the pre-determined reconfigurations and real-time disruptions in a flow line. A data-driven model predictive control (d-MPC) is developed for this problem such that the total expected cost for work-in-process inventory, final-product inventory, and tardiness penalty can be minimized. Considering the predictive multi-stage transitions and reconfigurable behaviors, d-MPC utilizes max-plus algebra to generate a time-varying state-space equation for a flow line. In this regard, a systematic method for permanent production loss identification can be formulated for real-time disruption diagnosis. This identification is useful in providing feedback signals for d-MPC and thus a closed-loop feedback control for PLT optimization can be achieved. A case study of an industrial welding line is reported to illustrate the operational benefits of d-MPC. The results demonstrate that d-MPC can efficiently cooperate with PLT optimization with machine reconfiguration such that the total expected cost can be minimized. Compared with continuous re-optimization, d-MPC adopts an event-driven mechanism to respond to real-time disruptions and can decrease the nervousness of flow lines with lower operational costs.

Density limits as disruption forecasters for spherical tokamaks

Fusion power output from spherical tokamaks would benefit from increased confined plasma density, but there exists a limit on the density before confinement is lost and the plasma current is disrupted. This density limit has long been characterized by a simple, global Greenwald limit proportional to the plasma current and inversely proportional to the cross sectional area of the plasma. It is shown that in the database of discharges from the National Spherical Tokamak Experiment (NSTX) and Mega Ampere Spherical Tokamak (MAST) spherical tokamaks, the likelihood of disruption does increase above the Greenwald limit, and especially in the plasma current rampdown phase. The physics of the density limit has been recently theoretically explored through local criteria. Several of these are tested using the disruption event characterization and forecasting (DECAFTM) code for their potential effectiveness as disruption warning signals. For a limited set of NSTX discharges, a local island power balance criteria was found to be less reliable, presently, than the Greenwald limit. An empirical critical edge line density and a boundary turbulent transport limit were both tested for MAST-U, which has an electron density profile measurement with high spatial resolution in the outer part of the plasma. Both were found to have similar dependencies on key plasma parameters. In a limited set of MAST-U discharges that appear to disrupt due to rising density at values under the Greenwald limit, crossing of the boundary turbulent transport limit occurred close to the time of disruption. Finally, these limits were evaluated for their potential use in real-time, and it was found that with the necessary real-time inputs and with refinement through further testing, these limits could be implemented in a real-time disruption forecasting system.

Internet-of-Things-augmented dynamic route planning approach to the airport baggage handling system

This paper proposes a dynamic route planning approach to the airport baggage handling systems. In previous studies, when planning the baggage transmission routes, real-time conditions of the baggage handling system are often neglected. This study incorporates the real-time baggage tracking information and device disruption information into the baggage route planning algorithm based on Internet-of-Things technologies. Taking Hierarchical Cooperative A* algorithm (HCA*) as the basic routing core, we propose a comprehensive algorithmic framework for the dynamic baggage route planning problem, in order to iteratively generate conflict-free baggage transmission routes while adapting to varying system conditions. We propose a unified prioritizing scheme so that baggage can be handled in proper order according to their arrival, departure and waiting time. This solution approach also contains a disruption handling mechanism by which the affected transportation links and baggage can be appropriately handled. A real airport case is used to verify the proposed solution approach. The results show that our proposed method is around 10% more efficient than the decentralized heuristic algorithm. Meanwhile, the average baggage throughput time decreases about 5% when real-time information is included. This paper also shows how to identify susceptible transmission links so specific maintenance plans can be arranged.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

Real-time disruption prediction in the plasma control system of HL-2A based on deep learning

A real-time disruption predictor based on deep learning method is implemented into the Plasma Control System (PCS) of HL-2A. It has a total accuracy of 89.0% during the online testing of Shot Nos. 38,650–39,347 in HL-2A. 32 false alarms are triggered during the 142 non-disruptive shots and 10 disruptions are missed in the 240 disruptive shots. In several shots, this disruption prediction algorithm is used to trigger the disruption mitigation system, namely, massive gas injection (MGI) and supersonic molecular beam injection (SMBI) in this experiment. For MGI, there is a 12-millisecond delay between the trigger signal and the mitigated disruption. In the 240 disruptive shots, 81.5% of them are predicted with more than 12 ms in advance and thus can be successfully mitigated. For SMBI, the delay is 4 millisecond and 91.3% of the disruptions can be successfully mitigated. In general, although dedicated work is needed to close the gap between online disruption prediction and the offline algorithms, the effectiveness of deep learning-based disruption predictors in real-time environments is validated in this research.

A Method for Granular Traffic Data Imputation Based on PARATUCK2

Imputing missing data is a critical task in data-driven intelligent transportation systems. During recent decades there has been a considerable investment in developing various types of sensors and smart systems, including stationary devices (e.g., loop detectors) and floating vehicles equipped with global positioning system (GPS) trackers to collect large-scale traffic data. However, collected data may not include observations from all road segments in a traffic network for different reasons, including sensor failure, transmission error, and because GPS-equipped vehicles may not always travel through all road segments. The first step toward developing real-time traffic monitoring and disruption prediction models is to estimate missing values through a systematic data imputation process. Many of the existing data imputation methods are based on matrix completion techniques that utilize the inherent spatiotemporal characteristics of traffic data. However, these methods may not fully capture the clustered structure of the data. This paper addresses this issue by developing a novel data imputation method using PARATUCK2 decomposition. The proposed method captures both spatial and temporal information of traffic data and constructs a low-dimensional and clustered representation of traffic patterns. The identified spatiotemporal clusters are used to recover network traffic profiles and estimate missing values. The proposed method is implemented using traffic data in the road network of Manhattan in New York City. The performance of the proposed method is evaluated in comparison with two state-of-the-art benchmark methods. The outcomes indicate that the proposed method outperforms the existing state-of-the-art imputation methods in complex and large-scale traffic networks.

A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter

ABSTRACT In this study, we develop a novel methodology to identify supply chain disruption events using Twitter feeds in real time. Underpinned by advances in Natural Language Processing (NLP) and machine learning, we propose an approach that includes a state-of-the-art variant of Conditional Random Field (CRF) model for event annotation, location-based clustering of the annotated events, and a fuzzy inference system to evaluate supply chain risk. We validate the new approach through a text corpus derived from a Twitter data stream, which is a popular method in NLP. The results show that the proposed model outperforms the baseline model.

Real-time prediction of high-density EAST disruptions using random forest

A real-time disruption predictor using random forest was developed for high-density disruptions and used in the plasma control system (PCS) of the EAST tokamak for the first time. The disruption predictor via random forest (DPRF) ran in piggyback mode and was actively exploited in dedicated experiments during the 2019–2020 experimental campaign to test its real-time predictive capabilities in oncoming high-density disruptions. During dedicated experiments, the mitigation system was triggered by a preset alarm provided by DPRF and neon gas was injected into the plasma to successfully mitigate disruption damage. DPRF’s average computing time of ∼250 μs is also an extremely relevant result, considering that the algorithm provides not only the probability of an impending disruption, i.e. the disruptivity, but also the so-called feature contributions, i.e. explainability estimates to interpret in real time the drivers of the disruptivity. DPRF was trained with a dataset of disruptions in which the electron density reached at least 80% of the Greenwald density limit, using the zero-dimensional signal routinely available to the EAST PCS. Through offline analysis, an optimal warning threshold on the DPRF disruptivity signal was found, which allows for a successful alarm rate of 92% and a false alarm rate of 9.9%. By analyzing the false alarm causes, we find that a fraction (∼15%) of the misclassifications are due to sudden transitions of plasma confinement from H- to L-mode, which often occur during high-density discharges in EAST. By analyzing DPRF feature contributions, it emerges that the loop voltage signal is that main cause of such false alarms: plasma signals more apt to characterize the confinement back-transition should be included to avoid false alarms.

Real-time disruption management approach for intermodal freight transportation

The share of intermodal transportation, which is often considered as a sustainable transportation alternative, is rather low compared to road transportation. There are several reasons for this situation, including the increased need for coordination of scheduled transport services and the reduced reliability of intermodal transport chains in case of disruptions. In this regard, developing an advanced algorithmic approach can help to handle real-time data during the execution of transportation and react adequately to detected unexpected events. In this way the reliability of intermodal transport can be increased, which might help to increase its usage and to minimize the negative externalities of freight transportation. This paper proposes a novel real-time decision support system based on a hybrid simulation-optimization approach for intermodal transportation which combines offline planning with online re-planning based on real-time data about unexpected events in the transportation network. For each detected disruption, the affected services and orders are identified and the best re-planning policy is applied. The proposed decision support system is successfully tested on real-life scenarios and is capable of delivering fast and reasonably good solutions in an online environment. This research might be of particular benefit to the transport industry for using advanced solution methodologies and give advice to transportation planners about the optimal policies that can be used in case of disruptions.

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.

A Real-Time Disruption Prediction Tool for VDE on EAST

Vertical displacement event (VDE) is one of the most important events related to disruptions. A real-time disruption prediction tool for VDE is presented in this article. Vertical growth rate is an essential parameter to describe vertical instability, which can be obtained based on the rigid plasma response model. A real-time vertical growth rate calculation system is built, which utilizes graphics process unit (GPU) parallel computation and reflective memory (RFM) boards communication with plasma control system (PCS). Based on real-time vertical growth rate calculation system, an alarm algorithm for detecting vertical instability is integrated into experimental advanced superconducting tokamak (EAST) PCS. It is designed to provide the capabilities of a switching control method and to trigger a warning signal in PCS when uncontrollable vertical instability is detected. Such a disruption prediction tool has been preliminarily applied in the 2019 EAST spring experiment.

Disruption Recovery for Urban Public Tram System: An Analysis of Replacement Service Selection

Disruption recovery is an imperative task for the tram system, which has caused widespread concerns in the public transport area. In this paper, how to provide replacement services for stranded passengers during short-term unplanned events has been investigated. A decision support tool for the tram company to dispatch appropriate replacement services by taxis or buses has been proposed. First, critical factors for disruption recovery decisions are identified as commuters' behaviors, recovery cost, and service level. Then, critical factors are model as replacement service selection strategies among bus, taxi, and bus-taxi hybrid. Finally, numerical analyses are conducted to shed light on key variables affecting the tram company's benefit function as well as replacement service decisions. The numerical analyses provide immediate managerial implications. The proposed method can be easily extended to real-time disruption recovery for urban public transit system by capturing passenger patterns and vehicle status through big data technologies.

Closed-loop Rescheduling using Deep Reinforcement Learning

Fil: Palombarini, Jorge Andres. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Centro de Investigaciones y Transferencia de Villa Maria. Universidad Nacional de Villa Maria. Centro de Investigaciones y Transferencia de Villa Maria; Argentina

Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption

The importance of acquiring and sharing real-time disruption information in the supply chain for proper deployment of disruption mitigation strategies is well-known in the literature. However, studies in this direction are limited in the domain of supply chain dynamics. In this paper, we investigate the effect of sharing real-time disruption and inventory information to mitigate supplier disruption through proper order allocation between the suppliers. We consider a three-echelon manufacturing supply chain network where a manufacturer and first-tier suppliers adopt dual sourcing. At the first-tier supplier level, the supply chain network is subjected to random disruption. Using control engineering modeling and simulation, we first evaluate the value of information sharing in disruption mitigation efforts, and further, we examine the effect of various control system design configurations of the manufacturer to maximize its dynamic performance in the information shared supply chain settings. The results show that, in the case of upstream supplier disruption, information transparency on the vulnerabilities among supply chain members improves the performance. Further, it is observed that for a given control structure, the selection of decision parameters affect the dynamic performance of the supply chain with proper order allocation strategy during the disruption. The findings of this research can provide the basis for managers to make informed decisions about using mitigation strategies with their supply chain partners.

Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch

Real-time (RT) disruption prediction (DP) is essential to trigger mitigation actions that avoid irreversible damage to the devices. This paper deals with disruption mitigation alarms and performs the RT implementation of a probabilistic predictor. The RT implementation has been carried out with a fast controller with DAQ FPGA-based data acquisition devices corresponding to ITER catalogue (in particular, a reconfigurable Input/Output platform has been used). Up to three input signals have been used and relevant information for the prediction is extracted from the temporal and the frequency domains. The signals are read from the JET database. Then D/A conversions are carried out and used as inputs to the real time system. In this way, the whole process of digitization, data analysis and prediction is performed. The computation time for each prediction takes less than 200 μs.

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

Real-time rescheduling and disruption management for public transit

ABSTRACTThis research is motivated by the operations of a public transit company in Hong Kong. We investigate how real-time information can be utilized in combination with historical data to improve routing and scheduling decisions practically. A dynamic integrated vehicle and crew scheduling problem is studied where travel times are stochastic and time-dependent. The objective is to maximize the route frequencies and mileage to provide good passenger service and simultaneously minimize crew overtime and meal-break delays. To mitigate unexpected delays due to uncertainties in operations, various mathematical models are proposed for revising the schedules in real time under a rolling-horizon framework. Their efficiency and effectiveness are evaluated via simulation using real-world data. The simulation results also identify the potential benefits of revising the schedule dynamically in real time using optimization models. The results show that the proposed approaches can significantly reduce motormen overtime and meal-break delays while maintaining coverage and route frequency requirements.

Passenger oriented railway disruption management by adapting timetables and rolling stock schedules

In passenger railway operations, unforeseen events require railway operators to adjust their timetable and their resource schedules. The passengers will also adapt their routes to their destinations. When determining the new timetable and rolling stock schedule, the railway operator has to take passenger behavior into account. The operator should increase the capacity of trains for which the operator expects more demand than on a regular day. Furthermore, the operator could increase the frequency of the trains that serve stations with an additional demand.This paper describes a real-time disruption management approach which integrates the rescheduling of the rolling stock and the timetable by taking the changed passenger demand into account. The timetable decisions are limited to additional stops of trains at stations at which they normally would not call. Several variants of the approach are suggested, with the difference in how to determine which additional stops should be executed.Real-time rescheduling requires fast solutions. Therefore a heuristic approach is used. We demonstrate the performance of the several variants of our algorithm on realistic instances of Netherlands Railways, the major railway operator in the Netherlands.